Yazar "Kilic, Seref" seçeneğine göre listele

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    Öğe
    Land-cover classification using advanced land observation satellite imagery: A case study of the peri-urban region of Antakya
    (WFL Publisher Ltd., 2013) Guzelmansur, Aysel; Kilic, Seref
    The aim of the study was to examine the potential maximum likelihood classification in the mapping of basic land cover/land use classes by using ALOS AVNIR-2 imagery. The two specific objectives were; (a) to develop a maximum likelihood classification scheme for mapping land cover/land use classes using ALOS AVNIR-2 imagery, (b) to estimate the accuracy of the used method. Land cover nomenclature is classified according to the Coordination of Information on the Environment (CORINE) Level 2 and 3 classifications. Ten urban land cover classes were used in this study: river, wetland vegetation, forest, mining area, shadow, mountain forest, cemetery, agriculture, built up area, industrial area. The classification accuracy was assessed using 218 pixels were stratified randomly distributed throughout the study area and independent of training sites used by the supervised classification algorithm. The results show that overall classification accuracies is 81.19% and overall kappa statistics is 0.7845.
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    Öğe
    Mapping soil drainage classes of Amik Plain using Landsat images
    (Academic Journals, 2009) Kilic, Seref
    Soil drainage is one of the important soil properties affecting plant growth, water transfer and solute transport in soils. Soil drainage is also an environmental component affecting irrigation and soil reclamation, land capability for agriculture, flood control systems, engineering, health and infectious diseases. The objective of this study was to map soil drainage classes by using Landsat image in Amik Plain (Hatay, Turkey). Terrain and vegetation are characterized by digital terrain attributes, and vegetation indices using a LANDSAT-7 Enhanced Thematic Mapper Image. The study benefits from five data sources: Landsat ETM image, topographic maps, soil maps, State Hydraulic Works (DSL) land cover records and ground data from field surveys. Image classification was carried out using Maximum Likelihood (ML) Classification with supervised training. Soil drainage classes were determined, thus finalizing the process of mapping after each mapping unit and drainage class prepared as a result of the ML classification were validated on site. According to the drainage map prepared using satellite image and ground data, 51,4% (37,234 ha) of Amik Plain are well drained and moderately well drained. 48,6% (35,192 ha) of Amik Plain are somewhat poorly drained, poorly drained, and very poorly drained.